Work Hours and Cognitive Function: The Multi-Ethnic Study of Atherosclerosis

Luenda E Charles; Desta Fekedulegn; Cecil M Burchfiel; Kaori Fujishiro; Adina Zeki Al Hazzouri; Annette L Fitzpatrick; Stephen R Rapp

doi:10.1016/j.shaw.2020.02.004

Work Hours and Cognitive Function: The Multi-Ethnic Study of Atherosclerosis

Saf Health Work. 2020 Jun;11(2):178-186. doi: 10.1016/j.shaw.2020.02.004. Epub 2020 Apr 23.

Authors

Luenda E Charles¹, Desta Fekedulegn¹, Cecil M Burchfiel¹, Kaori Fujishiro², Adina Zeki Al Hazzouri³, Annette L Fitzpatrick⁴, Stephen R Rapp⁵

Affiliations

¹ Bioanalytics Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Morgantown, WV, USA.
² Division of Surveillance, Hazard Evaluation, and Field Studies, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH, USA.
³ Department of Public Health Sciences, University of Miami, Miami, FL, USA.
⁴ Departments of Family Medicine, Epidemiology and Global Health, University of Washington, Seattle, WA, USA.
⁵ Department of Psychiatry and Behavioral Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Abstract

Background: Cognitive impairment is a public health burden. Our objective was to investigate associations between work hours and cognitive function.

Methods: Multi-Ethnic Study of Atherosclerosis (MESA) participants (n = 2,497; 50.7% men; age range 44-84 years) reported hours per week worked in all jobs in Exams 1 (2000-2002), 2 (2002-2004), 3 (2004-2005), and 5 (2010-2011). Cognitive function was assessed (Exam 5) using the Cognitive Abilities Screening Instrument (version 2), a measure of global cognitive functioning; the Digit Symbol Coding, a measure of processing speed; and the Digit Span test, a measure of attention and working memory. We used a prospective approach and linear regression to assess associations for every 10 hours of work.

Results: Among all participants, associations of hours worked with cognitive function of any type were not statistically significant. In occupation-stratified analyses (interaction p = 0.051), longer work hours were associated with poorer global cognitive function among Sales/Office and blue-collar workers, after adjustment for age, sex, physical activity, body mass index, race/ethnicity, educational level, annual income, history of heart attack, diabetes, apolipoprotein E-epsilon 4 allele (ApoE4) status, birth-place, number of years in the United States, language spoken at MESA Exam 1, and work hours at Exam 5 (β = -0.55, 95% CI = -0.99, -0.09) and (β = -0.80, -1.51, -0.09), respectively. In occupation-stratified analyses (interaction p = 0.040), we also observed an inverse association with processing speed among blue-collar workers (adjusted β = -0.80, -1.52, -0.07). Sex, race/ethnicity, and ApoE4 did not significantly modify associations between work hours and cognitive function.

Conclusion: Weak inverse associations were observed between work hours and cognitive function among Sales/Office and blue-collar workers.

Keywords: ApoE4, apolipoprotein E-epsilon 4 allele; BMI, body mass index; CASI, Cognitive Abilities Screening Instrument; CVD, cardiovascular disease; Cognitive abilities screening instrument; DS, Digit Span; DSC, Digit Symbol Coding; Digit Symbol Coding; GED, General Education Development; Global cognitive function; MESA, Multi-Ethnic Study of Atherosclerosis; MET-min, metabolic equivalent minutes; Occupation; Work schedule.

Grants and funding

P30 AG049638/AG/NIA NIH HHS/United States